Blood collection management: Methodology and application

Zahiri, Behzad; Torabi, S. Ali; Mousazadeh, Mohammad; Mansouri, S. Afshin

doi:10.1016/j.apm.2015.04.028

Cited by 81 publications

(31 citation statements)

References 45 publications

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“…Zahiri et al (2014a) performed another study which incorporates uncertainty to the blood-banking problem and they adopt a robust stochastic programming approach. In the following year, Zahiri et al (2015) focused on the blood distribution network design problem in which temporary and fixed facilities are located and donors are assigned to these points over a multi-period planning horizon while minimizing the overall cost.…”

Section: Related Literaturementioning

confidence: 99%

Organ transplantation logistics: a case for Turkey

et al. 2018

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Logistics is one of the key elements of organ transplantation operations. In this study, maximizing the potential compatible donor-recipient matches within the cold ischemia time bounds (duration that an organ can survive without blood supply) is the main problem that is addressed. While addressing this problem, the effects of clustering structures on the potential organ matches are investigated. We analyze Turkey's organ transplantation logistics structure based on its dynamics and provide new mathematical models for maximizing potential-weighted intra-regional organ transplantation flow via evaluating different types of transportation modes while meeting the specified time bounds. This approach considers only maximizing the potential flow within a single time bound, so that it may not perform effectively for every organ type. To remedy this situation, another mathematical model that maximizes the potential flow of multiple organ types has also been developed. Additionally, in order to evaluate the performance of our results, using the outputs of the deterministic mathematical models, we developed a simulation model to mimic the uncertain environment realistically while being able to model the components of hierarchical systems. Extensive computational analysis using a variety of performance measures has revealed that Turkey's organ transplantation network can be improved by re-clustering.

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Section: Related Literaturementioning

confidence: 99%

Organ transplantation logistics: a case for Turkey

et al. 2018

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“…The methods of the second class cope with ambiguous/imprecise parameters in the objective functions and constraints, which are mainly formulated by possibilistic distributions based on the available objective data and subjective information of the decision maker. Our model can be handled by methods of the second class due to it only includes imprecise parameters [51].…”

Section: Robust Optimizationmentioning

confidence: 99%

A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions

2018

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This paper introduces a joint location-inventory problem, in which facilities become temporarily unavailable. A hybrid approach based on the Markov process and mathematical programming techniques is presented to design the distribution network of a supply chain in an integrated manner. In the first phase, the Markov process derives some performance features of inventory policy. In the second phase, using outputs of the Markov process, the location-inventory problem is formulated as a mixed-integer nonlinear programming model. Moreover, a robust possibilistic programming approach is utilized, which is able to provide a more stable supply chain structure under almost all possible values of imprecise parameters. Since the proposed problem is complicated to solve by means of exact methods, we develop a simulated annealing algorithm in order to find near-optimal solutions in reasonable computational times. The obtained computational results reveal the efficiency and effectiveness of the proposed solution approach. Finally, some insights are provided and the performance of the proposed robust optimization approach is compared to traditional possibilistic chance constrained method.

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“…Nevertheless, most proposed HLP models treat data as known and deterministic. Hence, many researchers have attempted to model the uncertainty in the design of optimization problems in recent studies [25][26][27][28][29][30][31][32][33][34] .…”

Section: Robust Srhlpmentioning

confidence: 99%

Design of a reliable logistics network with hub disruption under uncertainty

Mohammadi

Tavakkoli–Moghaddam

Siadat

et al. 2016

Applied Mathematical Modelling

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a b s t r a c tIn this study, we design a reliable logistics network based on a hub location problem, which is less sensitive to disruption and it performs efficiently when disruption occurs. A new mixed-integer programming model is proposed to minimize the total sum of the nominal and expected failure costs. This model considers complete and partial disruption at hubs. In addition, we propose a new hybrid meta-heuristic algorithm based on genetic and imperialist competitive algorithms. We compare the performance of the proposed algorithm with a new lower bound method in terms of the CPU time and solution quality. Furthermore, we conclude that a considerable improvement in the reliability of the network can be achieved with only a slight increase in the total cost. Finally, we demonstrate that the networks designed using our model are less conservative and more robust to disruption compared with those designed based on other robustness measures.

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Blood collection management: Methodology and application

Cited by 81 publications

References 45 publications

Organ transplantation logistics: a case for Turkey

Organ transplantation logistics: a case for Turkey

A hybrid Markov process-mathematical programming approach for joint location-inventory problem under supply disruptions

Design of a reliable logistics network with hub disruption under uncertainty

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